Formula Optimization of 100 mg Chewable Ascorbic Acid Tablets
Why this work is in the frame
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Bibliographic record
Abstract
Background: Ascorbic acid is a water soluble nigh dose drug that usually degrades in the presence of moisture with the formation of not so biologically active substances.. Pharmaceutical excipients have long been used to impart functionalities that improve stability and enhance patient compliance while increasing cost. Optimization therefore aims at achieving a compromise between a given set of constraints that yields the best formulation. Objectives: The aim of this work is to produce optimised formulation of 100 mg chewable ascorbic acid tablet. Methods: The lubricant was stearic acid at 0.25 %, 0.5% or 0.75 %. The direct compression excipient (DCE) used was Avicel® PH 102 with sorbitol as sweetener in the ratios of sorbitol to Avicel of 1:0, 0:1, 1:1 1:2, 1:3, 1: 4 respectively. The tablet weight was calculated such that the concentration of drug is 30-50% of the direct compression excipient (DCE). A step-wise optimization approach was employed. The best batch was selected as having the highest DCE dilution, hardness ≥4 kgf, minimal tablet defects, and acceptable weight variation, content and content variation. Results: The optimal formula was obtained with the batch that has the following formula, 0.75 % stearic acid at a maximum DCE ratio of avicel: sorbitol of 4 :1 at dilution of 40 % w/w. The flow rate of the powder mix for this batch was 29.70 g/s, with Carr's compressibility index of 22%, and Hausner ratio of 1.28. The angle of o repose determined by free flow from a height of 4 cm was 23 . Drug-excipient compatibility studies using DSC revealed no significant interaction between the tablet components except possible change in crystal structure. Conclusion: The optimal formulation had the following formula: 0.75 % stearic acid, 4 Avicel: 1 sorbitol, and at a maximum DCE dilution of 40% w/w.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it